实施一个实时复杂事件流处理系统，以帮助识别临床和转化研究中的潜在参与者。
Implementing a Real-time Complex Event Stream Processing System to Help Identify Potential Participants in Clinical and Translational Research Studies.
作者信息
Weber Susan, Lowe Henry J, Malunjkar Sanjay, Quinn James
机构信息
Center for Clinical Informatics.
出版信息
AMIA Annu Symp Proc. 2010 Nov 13;2010:472-6.
Abstract
Event Stream Processing is a computational approach to the problem of how to infer the occurrence of an event from a data stream in real time without reference to a database. This paper describes how we implemented this technology on the STRIDE platform to address the challenge of real time notification of patients presenting in the Emergency Department (ED) who potentially meet eligibility criteria for a clinical study. The system was evaluated against a standalone legacy alerting system and found to perform adequately. While our initial use of this technology was focused on relatively simple alerts, the system is extensible and has the potential to provide enterprise-level research alerting services supporting more complex scenarios.
摘要
事件流处理是一种计算方法，用于解决如何在不参考数据库的情况下从数据流中实时推断事件发生的问题。本文描述了我们如何在STRIDE平台上实现这项技术，以应对实时通知急诊科中可能符合临床研究资格标准的患者这一挑战。该系统与一个独立的传统警报系统进行了评估，结果表明其性能足够。虽然我们最初对这项技术的使用集中在相对简单的警报上，但该系统具有可扩展性，有潜力提供支持更复杂场景的企业级研究警报服务。
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